code: def __init__(self, args: dict):
      BaseMetric.__init__(self)
  
      if args.input_dim is None:
          raise AttributeError(
              "No input dimension provided !"
              "'input_dim' parameter of ModelConfig instance must be set to 'data_shape' where "
              "the shape of the data is [mini_batch x data_shape]. Unable to build metric "
              "automatically"
          )
  
      self.input_dim = args.input_dim
      self.latent_dim = args.latent_dim
  
      self.layers = nn.Sequential(
          nn.Linear(np.prod(args.input_dim), 400), nn.ReLU()
      )
      self.diag = nn.Linear(400, self.latent_dim)
      k = int(self.latent_dim * (self.latent_dim - 1) / 2)
      self.lower = nn.Linear(400, k)
  
imports: from typing import List
  import numpy
  import torch
  import torch.nn
  from pythae.models.nn import BaseDecoder, BaseDiscriminator, BaseEncoder, BaseMetric
  from ..base.base_utils import ModelOutput
label_desc: Use image node_id 1 to create a new Metric_MLP object from inherited base classes: BaseMetric with example: obj = Metric_MLP(args)